P06 MODELING DYNAMIC EVOLUTION OF MULTIPLE MYELOMA STUDYING CELL POPULATIONS METABOLISM AND COMPETITION UNDER MAXIMUM POWER CONSTRAINTS: A SYSTEMS-THINKING BASED APPROACH
Authors: A. Romano, L. Conte, A. Giansanti, A. Kleidon, F. Gonella
Journal: HemaSphere (2023)

Several groups are attempting to integrate clinical and biological variables arising from genomics, microenvironment and immune function in Multiple myeloma (MM) evolutionary path. However, it is hard to develop a taxonomy that could take in account the intra-patient heterogeneity of neoplastic plasma cells (PCs), their interaction with microenvironment and emerging cell-extrinsic factors to predict the disease evolution in each individual patient. Ecological interactions between different cell stocks are recognized as fundamental at the mesoscale, addressing the possibility of a description that uses language and concepts of ecology to address their complex interaction dynamics. Nevertheless, a comprehensive biophysical framework encompassing growth, adaptation and survival of cell populations is still lacking to describe the dynamic evolution of MM. Taking advantage of mathematical modelling and system dynamics, we propose to describe the evolution of MGUS into MM as a unique complex system in dynamic balance. Dynamic models, where the temporal evolution of extensive variables can be simulated in the form of configurations, derive their initial conditions from available and assessed evidence. Varying the key system parameters, trajectories represent the possible evolutive (structural) patterns of the system at issue, becoming abstracted with respect to local specific attributes related to single case studies. By a properly identified physical-based state space, we show a method to classify cell population growth regimes under limited resource availability from measures of ATP power generation and dissipation. The maximum power state naturally emerges as the thermodynamic limit for such constrained systems. The competition for limited space between different phenotypes of antagonist populations exhibits a class of regime shifts to more stable and dissipative states for selective advantaged populations operating closer to the thermodynamic limit. The critical time for the transitions is controlled by the strength of the actual biochemical interaction. At time of MM onset, the presence of several accountable variables confers a complex network of feedbacks leading to specific systemic configurations, which can evolve to maximize the persistence of neoplastic PCs, thus changing the system structure, output, and purpose (a pattern that we call in clinics Multiple Myeloma) or to be so resilient to co-evolve with neoplastic PCs (a pattern that we call in clinics MGUS or sMM). At any timepoint, under the pressure of external driver forces, which cannot be evaluated separately (e.g. new mutations, immune system dysregulation, inflammaging), the dynamic configurations could evolve to achieve a new stationary status in a pattern which identifies a new disease phase, which could still evolve in presence of additional external driver forces and so on. Thus, our modeling approach is relevant for biophysical and biomedical research as it represents a novelty in the description of MM evolution, using systemic stock-flow diagrams that address feedback mechanisms and trade-offs operating at the cell population level.